Advancing Blockchain Scalability: An Introduction to Layer 1 and Layer 2 Solutions
Not on the map. Why?
Abstract (arXiv)
Bitcoin rise has put blockchain technology into the mainstream, amplifying its potential and broad utility. While Bitcoin has become incredibly famous, its transaction rate has not match such a corresponding increase. It still takes approximately 10 minutes to mine a block and add it to the chain. This limitation highlights the importance of seeking scale-up solutions that solve the low throughput transaction rates. Blockchain's consensus mechanisms make peer-to-peer transactions becomes feasible and effectively eliminate the need for centralized control. However, the decentralized systems also causes a lower speed and throughput compared to centralized networks as we mentioned Bitcoin's block creation rates. Two mainstreams scale-up solutions, Layer 1 scale-up and Layer 2 scale-up have been implemented to address these issues. Layer 1 level scalability enhancements happen at where traditional blockchain operates. This paper provides a deep examination of the components of the Layer 1 protocol and the scale-up methods that directly improve the lower level blockchain. We also address that Layer 1 solutions encounter inherent limitations although improvements were applied due to layer 1 storage costs and latency are high. In addition, we discuss layer 2 protocols, advanced scalability techniques, that elevate blockchain performance by handling transactions off the mainnet. Our findings indicate that Layer 2 protocols, with their various implementations such as rollups and channels, significantly outperform Layer 1 solutions in terms of transaction throughput and efficiency. This paper discusses these Layer 2 scaling methods in detail, aiming to provide readers with a comprehensive understanding of these protocols and the underlying logic that drives their effectiveness.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Jun 2024.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2024: 2; 2025: 2.
4 papers.
- 2024A Comprehensive Study on Quantization Techniques for Large Language Modelscited by 36
- 2024Blockchain for Large Language Model Security and Safety: A Holistic Surveycited by 8
- 2025A Comprehensive Review on Noise Control of Diffusion Modelcited by 4
- 2025AI-Based Crypto Tokens: The Illusion of Decentralized AI?cited by 2
- 2025AI-Based Crypto Tokens: The Illusion of Decentralized AI?29 Apr 2025
- 2025A Comprehensive Review on Noise Control of Diffusion Model7 Feb 2025
- 2024A Comprehensive Study on Quantization Techniques for Large Language Models30 Oct 2024
- 2024Blockchain for Large Language Model Security and Safety: A Holistic Survey26 Jul 2024